Do factors other than need determine utilization of physicians' services in Ontario?
Bibliographic record
Abstract
BACKGROUND: Universal health care systems seek to ensure access to care on the basis of need, rather than income, but there are concerns about preferential access to cardiovascular and specialist care for high income patients. In this study, I used population-based, individual-level health, income and utilization data to determine whether whether there is evidence for differential access to physician care in relation to household income. METHODS: I studied data for 2170 Ontario respondents to the 1995 National Population Health Survey (aged 40 to 79 years) who had approved linkage of their survey responses to the administrative databases of the Ontario Health Insurance Plan and for whom income data were available. I used linear and generalized linear regression to model the mean per capita expenditures on physician care and the probability of referral to a specialist in relation to income and self-reported health status. RESULTS: Residents of higher income households incurred lower per capita expenditures for physicians' services than those in lower income households; for example, the mean per capita expenditure in the upper middle income group was $220 less (95% confidence interval -$87 to -$334) than the mean per capita expenditure in the lowest income group. Expenditures were significantly related to self-reported health status; for example, the mean per capita expenditure among those reporting fair health status was $590 higher (95% confidence interval $465 to $737) than among those reporting excellent health. After adjustment for health status, there was no association between income and the expenditures on all physician services, out-of-hospital services or specialist care. INTERPRETATION: Utilization of physicians' services in Ontario is based on need, rather than income.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".